Compare Transcript TPMs - Experiments

Overview

Here we compare mESCs from four datasets:

• Lackford et al., 2014 (article | data)
• Nam et al., 2014 (article | data)
• Guo et al., 2019 (article | data)
• Bleckwehl et al., 2021 (preprint | data)

Data Description

Cell Cultures

The Lackford data is based on E14Tg2a cells (129/Ola strain) cultured in ESGRO Complete PLUS Clonal Grade Medium (Millipore), which uses a specific (but proprietary) GSK3β inhibitor and a serum-free condition that does not require LIF.

The Nam data lists 129S5/SvEvBrd as the strain. Culture conditions are not specified in article, supplement, GEO, or SRA.

The Guo data is based on OG2 transgenic mouse embryonic stem cells, with a strain background of 1/2 129SVJ+3/8 C57B6+1/8 CBA. Cells were cultured on feeder layers with 2iL medium (high glucose DMEM, 15%FBS, NEAA, GlutaMAX, LIF) plus 2i (1 mm PD0325901, 3 mm CHIR99021) and LIF.

The Bleckwehl data is based on E14Tg2a cells (129/Ola strain) cultured in 2i+LIF conditions (serum-free N2B27 medium supplemented with MEK inhibitor PD0325901 [0.4 μM, Miltenyi Biotec], GSK3β inhibitor CHIR99021 [3 μM, Amsbio], and LIF).

Library Preparation and Sequencing

Lackford used a direct RNA sequencing protocol performed by Helicos BioSciences.

Nam used a 3P-Seq protocol based on Jan et al., 2011, that uses 16-20 amplification cycles.

Bleckwehl and Guo used a 10X Chromium 3’ sequencing kit (v2 chemistry).

Quantification

Lackford and Nam quantifications come from PolyASite v2 (pipeline).

Bleckwehl and Guo are quantified through the scutr-quant pipeline.

Methods

## Loading PolyASite BED files
mm10_seqinfo <- seqinfo(getBSgenome("mm10")) %>% keepStandardChromosomes

extra_cols_polyasite <- c(pct_samples="numeric", n_protocols="integer",
                          mean_tpm="numeric", region="factor", pas="character")

import_polyasite <- function(...) {
  import(..., format="BED", extraCols=extra_cols_polyasite) %>% 
    mutate(tpm=score) %>%
    select(name, tpm, 3:7) %>%
    `seqlevelsStyle<-`("UCSC") %>%
    `seqlevels<-`(value=seqlevels(mm10_seqinfo), pruning.mode='coarse') %>%
    `seqinfo<-`(value=mm10_seqinfo)
}

lower_geom_point_pct <- function (data, mapping, ...) {
  ggplot(data=data, mapping=mapping, ...) +
    geom_point(size=0.5, alpha=0.5, pch=16, ...) +
    geom_abline(linetype='dashed', color='red') +
    lims(x=c(0,1), y=c(0,1))
}

lower_geom_point <- function (data, mapping, ...) {
  ggplot(data=data, mapping=mapping, ...) +
    geom_point(size=0.5, alpha=0.5, pch=16, ...) +
    geom_abline(linetype='dashed', color='red')
}

Load Data

bleckwehl_samples=c("2i_ESC"="Bleckwehl21", "d2_EB"="Bleckwehl21_EB2", "d4_EB"="Bleckwehl21_EB4")

gr_nam <- import_polyasite("data/bed/polyasite/nam14/mESC_WT.bed.gz")
gr_lackford <- import_polyasite("data/bed/polyasite/lackford14/mESC_WT.bed.gz")

sce_bleckwehl <- readRDS("data/sce/01_filtered.rada-iglesias20.utrome.txs.Rds") %>%
  `[`(, .$sample %in% names(bleckwehl_samples))

sce_guo <- readRDS("data/sce/01_filtered.guo19.utrome.txs.Rds")

Merge Single Cell

colData(sce_bleckwehl) %<>% 
  as_tibble(rownames='cell_id') %>%
  mutate(sample_id=bleckwehl_samples[sample]) %>%
  select(cell_id, sample_id) %>%
  column_to_rownames("cell_id") %>%
  DataFrame()

colData(sce_guo) %<>%
  as_tibble %>%
  mutate(sample_id="Guo19") %>%
  select(cell_id, sample_id) %>%
  column_to_rownames("cell_id") %>%
  DataFrame()

sce <- cbind(sce_guo, sce_bleckwehl)

Filter Data

# Filter to top two non-overlapping UTRs
df_utr2 <- rowData(sce) %>%
  as_tibble %>%
  select(transcript_id, gene_id, gene_symbol, is_ipa, atlas.utr_type, is_blacklisted) %>%
  mutate(total_umis=rowSums(counts(sce)),
         tpm=total_umis*1e6/sum(total_umis, na.rm=TRUE)) %>%
  #filter(!is_ipa, total_umis > 0) %>%
  filter(!is_ipa, atlas.utr_type == 'multi', !is_blacklisted) %>%
  group_by(gene_id) %>%
  filter(dplyr::n() > 1) %>%
  mutate(utr_rank=row_number(-total_umis)) %>%
  ungroup() %>%
  filter(utr_rank %in% 1:2)

idx_utr2 <- rowRanges(sce)[df_utr2$transcript_id,] %>%
  join_overlap_self_directed() %>%
  filter(transcript_id != transcript_id.overlap) %>%
  { filter(df_utr2, !(transcript_id %in% .$transcript_id)) } %>%
  group_by(gene_id) %>%
  filter(n() == 2) %>%
  ungroup() %$%
  transcript_id

TPM

Compute Bulk

df_nam <- gr_nam %>%
  anchor_center() %>%
  mutate(width=50) %>% 
  join_overlap_intersect_directed(x=rowRanges(sce)[idx_utr2,]) %>%
  group_by(transcript_id) %>%
  summarise(tpm=sum(tpm, na.rm=TRUE)) %>%
  as_tibble() %>%
  rename(Nam14=tpm)

df_lackford <- gr_lackford %>%
  anchor_center() %>%
  mutate(width=50) %>% 
  join_overlap_intersect_directed(x=rowRanges(sce)[idx_utr2,]) %>%
  group_by(transcript_id) %>%
  summarise(tpm=sum(tpm, na.rm=TRUE)) %>%
  as_tibble() %>%
  rename(Lackford14=tpm)

Compute Single-Cell

M_samples <- fac2sparse(sce$sample_id) %>% t

cts <- assay(sce, 'normcounts') %*% M_samples

df_sc <- (cts %*% Diagonal(ncol(cts), 1e6/colSums(cts))) %>% 
  `[`(idx_utr2, , drop=FALSE) %>% 
  as.matrix %>% 
  as_tibble(rownames='transcript_id')

Merge Data

df_tpm <- df_sc %>%
  left_join(df_nam, by='transcript_id') %>%
  left_join(df_lackford, by='transcript_id') %>%
  mutate(across(where(is.numeric), coalesce, 0)) %>%
  left_join(select(df_utr2, transcript_id, gene_id, total_umis, utr_rank), 
            by='transcript_id') %>%
  select(transcript_id, gene_id, utr_rank, total_umis, everything())

N_SAMPLES=ncol(df_tpm) - 5
SAMPLE_COLS=c("Nam14", "Lackford14", "Guo19", "Bleckwehl21")
#SAMPLE_COLS=c("Nam14", "Lackford14", "Guo19", "Bleckwehl21", "Bleckwehl21_EB2", "Bleckwehl21_EB4")
MIN_TPM=0

df_tpm_min <- df_tpm %>%
  pivot_longer(all_of(SAMPLE_COLS), names_to="sample", values_to="tpm") %>%
  group_by(transcript_id) %>%
  filter(all(tpm > MIN_TPM)) %>%
  ungroup() %>%
  pivot_wider(names_from='sample', values_from='tpm') %>%
  group_by(gene_id) %>%
  filter(n() == 2) %>%
  ungroup()

Combined Figures

TPM

All genes

df_tpm %>%
  mutate(across(all_of(SAMPLE_COLS), . %>% { log10(1+.) })) %>%
  ggpairs(columns=SAMPLE_COLS, progress=FALSE,
          lower=list(continuous=wrap(lower_geom_point, alpha=0.2)),
          upper=list(continuous=wrap('cor', method="spearman"))) +
  ##        lower=list(continuous=wrap("points", alpha=0.2, size=0.2, pch=16))) +
  
  labs(x="log10(1 + TPM)", y="log10(1 + TPM)") +
  theme_bw()

ggsave("output/figure2/ed2f-mescs-all-tpm.pdf", width=6, height=6, dpi=300)

Two-UTR Genes

df_tpm_min %>%
  mutate(across(all_of(SAMPLE_COLS), . %>% { log10(1+.) })) %>%
  ggpairs(columns=SAMPLE_COLS, progress=FALSE,
          lower=list(continuous=wrap(lower_geom_point, alpha=0.2)),
          upper=list(continuous=wrap('cor', method="spearman"))) +
          ##lower=list(continuous=wrap("points", alpha=0.2, size=0.2, pch=16))) +
  labs(x="log10(1 + TPM)", y="log10(1 + TPM)") +
  theme_bw()

LUI

Compute

df_lui <- df_tpm %>% 
  pivot_longer(all_of(SAMPLE_COLS), names_to='sample', values_to="tpm") %>%
  group_by(gene_id, sample) %>%
  mutate(lui=tpm/sum(tpm, na.rm=TRUE),
         utr_pos=row_number(as.integer(str_extract(transcript_id,
                                                   "[^.]+$")))) %>%
  filter(utr_pos == max(utr_pos)) %>% 
  ungroup() %>%
  select(gene_id, sample, lui) %>%
  pivot_wider(names_from='sample', values_from='lui')

df_lui_min <- df_tpm_min %>% 
  pivot_longer(all_of(SAMPLE_COLS), names_to='sample', values_to="tpm") %>%
  group_by(gene_id, sample) %>%
  mutate(lui=tpm/sum(tpm, na.rm=TRUE),
         utr_pos=row_number(as.integer(str_extract(transcript_id,
                                                   "[^.]+$")))) %>%
  filter(utr_pos == max(utr_pos)) %>% 
  ungroup() %>%
  select(gene_id, sample, lui) %>%
  pivot_wider(names_from='sample', values_from='lui')

SAMPLE_COLS_LUI=seq(2, ncol(df_lui))

Plot All Multi-UTR

df_lui %>%
  ggpairs(columns=SAMPLE_COLS_LUI, progress=FALSE,
          lower=list(continuous=wrap(lower_geom_point_pct, alpha=0.4)),
          upper=list(continuous=wrap('cor', method="spearman"))) +
          ##lower=list(continuous=wrap("points", alpha=0.3, size=0.1))) +
  labs(x="LUI", y="LUI") +
  theme_bw()

Plot Two-UTR

df_lui_min %>%
  ggpairs(columns=SAMPLE_COLS_LUI, progress=FALSE,
          lower=list(continuous=wrap(lower_geom_point_pct, alpha=0.4)),
          upper=list(continuous=wrap('cor', method="spearman"))) +
          ##lower=list(continuous=wrap("points", alpha=0.3, size=0.1))) +
  labs(x="LUI", y="LUI") +
  theme_bw()

ggsave("output/figure2/ed2h-mescs-all-lui-two-utr.pdf", width=6, height=6, dpi=300)

Exported Figures

Bulk Compare

TPM

axis_lims <- c(df_tpm$Lackford14, df_tpm$Nam14) %>% 
  { log10(1 + .) } %>% { c(min(.), max(.)) }
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"", 
                       cor(df_tpm$Nam14, df_tpm$Lackford14, method='spearman'))
label_lims <- axis_lims %>% { . + c(0.25*(.[2]-.[1]), 0.05*(.[1]-.[2])) }

df_tpm %>% 
  ggplot(aes(x=log10(Lackford14+1), y=log10(Nam14+1))) +
  geom_point(size=0.3, alpha=0.4, pch=16) +
  geom_abline(linetype='dashed', color='red') +
  annotate("text", x=label_lims[1], y=label_lims[2], label=label_rho_s, 
           size=4, parse=TRUE) +
  scale_x_continuous(limits=axis_lims) + scale_y_continuous(limits=axis_lims) +
  labs(x="Bulk - Lackford14 [log10(TPM + 1)]", y="Bulk - Nam14 [log10(TPM + 1)]") +
  theme_bw() +
  theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)

ggsave("output/figure2/fig2g-mescs-bulk-bulk-tpm.pdf", width=5, height=4)

LUI

label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"", 
                       cor(df_lui_min$Nam14, df_lui_min$Lackford14, method='spearman'))

df_lui_min %>% 
  ggplot(aes(x=Lackford14, y=Nam14)) +
  geom_point(size=0.4, alpha=0.5, pch=16) +
  geom_abline(linetype='dashed', color='red') +
  annotate("text", x=0.25, y=0.95, label=label_rho_s, size=4, parse=TRUE) +
  scale_x_continuous(limits=c(0,1), expand=c(0,0)) +
  scale_y_continuous(limits=c(0,1), expand=c(0,0)) +
  labs(x="Bulk - Lackford14 [LUI]", y="Bulk - Nam14 [LUI]") +
  theme_bw() +
  theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)

ggsave("output/figure2/fig2j-mesc-bulk-bulk-lui-two-utr.pdf", width=5, height=4)

Bulk vs scRNA-seq

TPM

axis_lims <- c(df_tpm$Lackford14, df_tpm$Bleckwehl21) %>%
  { log10(1 + .) } %>% { c(min(.), max(.)) }
label_lims <- axis_lims %>% { . + c(0.25*(.[2]-.[1]), 0.05*(.[1]-.[2])) }
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"", 
                       cor(df_tpm$Lackford14, df_tpm$Bleckwehl21, method='spearman'))

df_tpm %>% 
  ggplot(aes(x=log10(Lackford14+1), y=log10(Bleckwehl21+1))) +
  geom_point(size=0.3, alpha=0.4, pch=16) +
  geom_abline(linetype='dashed', color='red') +
  annotate("text", x=label_lims[1], y=label_lims[2], label=label_rho_s, 
           size=4, parse=TRUE) +
  scale_x_continuous(limits=axis_lims) + scale_y_continuous(limits=axis_lims) +
  labs(x="Bulk - Lackford14 [log10(TPM + 1)]", y="scRNA-seq - Bleckwehl21 [log10(TPM + 1)]") +
  theme_bw() +
  theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)

ggsave("output/figure2/fig2h-mescs-bulk-sc-tpm.pdf", width=5, height=4)

LUI

label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"", 
                       cor(df_lui_min$Lackford14, df_lui_min$Bleckwehl21, method='spearman'))

df_lui_min %>% 
  ggplot(aes(x=Lackford14, y=Bleckwehl21)) +
  geom_point(size=0.4, alpha=0.5, pch=16) +
  geom_abline(linetype='dashed', color='red') +
  annotate("text", x=0.25, y=0.95, label=label_rho_s, size=4, parse=TRUE) +
  scale_x_continuous(limits=c(0,1), expand=c(0,0)) +
  scale_y_continuous(limits=c(0,1), expand=c(0,0)) +
  labs(x="Bulk - Lackford14 [LUI]", y="scRNA-seq - Bleckwehl21 [LUI]") +
  theme_bw() +
  theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)

ggsave("output/figure2/fig2k-mesc-bulk-sc-lui-two-utr.pdf", width=5, height=4)

scRNA-seq Compare

TPM

axis_lims <- c(df_tpm$Guo19, df_tpm$Bleckwehl21) %>%
  { log10(1 + .) } %>% { c(min(.), max(.)) } 
label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"", 
                       cor(df_tpm$Guo19, df_tpm$Bleckwehl21, method='spearman'))
label_lims <- axis_lims %>% { . + c(0.25*(.[2]-.[1]), 0.05*(.[1]-.[2])) }

df_tpm %>% 
  ggplot(aes(x=log10(Guo19+1), y=log10(Bleckwehl21+1))) +
  geom_point(size=0.2, alpha=0.3, pch=16) +
  geom_abline(linetype='dashed', color='red') +
  annotate("text", x=label_lims[1], y=label_lims[2], label=label_rho_s, size=4, parse=TRUE) +
  scale_x_continuous(limits=axis_lims) + scale_y_continuous(limits=axis_lims) +
  labs(x="scRNA-seq - Guo19 [log10(TPM + 1)]", y="scRNA-seq - Bleckwehl21 [log10(TPM + 1)]") +
  theme_bw() +
  theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)

ggsave("output/figure2/fig2i-mescs-sc-sc-tpm.pdf", width=5, height=4)

LUI

label_rho_s <- sprintf("\"Spearman\" ~ rho == \"%0.3f\"", 
                       cor(df_lui_min$Guo19, df_lui_min$Bleckwehl21, method='spearman'))

df_lui_min %>% 
  ggplot(aes(x=Guo19, y=Bleckwehl21)) +
  geom_point(size=0.4, alpha=0.5, pch=16) +
  geom_abline(linetype='dashed', color='red') +
  annotate("text", x=0.25, y=0.95, label=label_rho_s, size=4, parse=TRUE) +
  scale_x_continuous(limits=c(0,1), expand=c(0,0)) +
  scale_y_continuous(limits=c(0,1), expand=c(0,0)) +
  labs(x="scRNA-seq - Guo19 [LUI]", y="scRNA-seq - Bleckwehl21 [LUI]") +
  theme_bw() +
  theme(plot.margin=grid::unit(c(4,8,4,4), "mm"), aspect.ratio=1)

ggsave("output/figure2/fig2l-mesc-sc-sc-lui-two-utr.pdf", width=5, height=4)

---

Runtime Details

Session Info

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_12/lib/libopenblasp-r0.3.10.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] BSgenome.Mmusculus.UCSC.mm10_1.4.0 GGally_2.1.0                      
##  [3] cowplot_1.1.0                      forcats_0.5.1                     
##  [5] stringr_1.4.0                      dplyr_1.0.5                       
##  [7] purrr_0.3.4                        readr_1.4.0                       
##  [9] tidyr_1.1.3                        tibble_3.1.0                      
## [11] ggplot2_3.3.3                      tidyverse_1.3.0                   
## [13] magrittr_2.0.1                     Matrix_1.3-2                      
## [15] BSgenome_1.58.0                    Biostrings_2.58.0                 
## [17] XVector_0.30.0                     SingleCellExperiment_1.12.0       
## [19] SummarizedExperiment_1.20.0        Biobase_2.50.0                    
## [21] MatrixGenerics_1.2.0               matrixStats_0.58.0                
## [23] plyranges_1.10.0                   rtracklayer_1.50.0                
## [25] GenomicRanges_1.42.0               GenomeInfoDb_1.26.4               
## [27] IRanges_2.24.0                     S4Vectors_0.28.0                  
## [29] BiocGenerics_0.36.0               
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6             fs_1.5.0                 lubridate_1.7.10        
##  [4] RColorBrewer_1.1-2       httr_1.4.2               tools_4.0.2             
##  [7] backports_1.2.1          utf8_1.1.4               R6_2.5.0                
## [10] DBI_1.1.1                colorspace_2.0-0         withr_2.4.1             
## [13] tidyselect_1.1.0         compiler_4.0.2           cli_2.3.1               
## [16] rvest_1.0.0              xml2_1.3.2               DelayedArray_0.16.0     
## [19] labeling_0.4.2           scales_1.1.1             digest_0.6.27           
## [22] Rsamtools_2.6.0          rmarkdown_2.7            pkgconfig_2.0.3         
## [25] htmltools_0.5.1.1        highr_0.8                dbplyr_2.1.0            
## [28] rlang_0.4.10             readxl_1.3.1             rstudioapi_0.13         
## [31] generics_0.1.0           farver_2.1.0             jsonlite_1.7.2          
## [34] BiocParallel_1.24.0      RCurl_1.98-1.2           GenomeInfoDbData_1.2.4  
## [37] Rcpp_1.0.6               munsell_0.5.0            fansi_0.4.2             
## [40] lifecycle_1.0.0          stringi_1.5.3            yaml_2.2.1              
## [43] zlibbioc_1.36.0          plyr_1.8.6               grid_4.0.2              
## [46] crayon_1.4.1             lattice_0.20-41          haven_2.3.1             
## [49] hms_1.0.0                knitr_1.31               pillar_1.5.1            
## [52] reprex_1.0.0             XML_3.99-0.5             glue_1.4.2              
## [55] evaluate_0.14            modelr_0.1.8             vctrs_0.3.6             
## [58] cellranger_1.1.0         gtable_0.3.0             reshape_0.8.8           
## [61] assertthat_0.2.1         xfun_0.20                broom_0.7.5             
## [64] GenomicAlignments_1.26.0 ellipsis_0.3.1

Conda Environment

## Conda Environment YAML
name: bioc_3_12
channels:
  - merv
  - conda-forge
  - bioconda
  - defaults
dependencies:
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